Flood Monitoring System Research Articles

Traditional and Novel Methods of Rainfall Observation and Measurement: A Review

Abstract Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowdsourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems.

Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach

The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), which simulates the stream’s discharge. Considering the HEC-HMS’s estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods.

Assessing the ability of a new seamless short-range ensemble rainfall product to anticipate flash floods in the French Mediterranean area

Abstract. Flash floods have dramatic economic and social consequences, and efficient adaptation policies are required to reduce their impacts, especially in the context of global change. Developing more efficient flash flood forecasting systems can largely contribute to these adaptation requirements. The aim of this study was to assess the ability of a new seamless short-range ensemble quantitative precipitation forecast (QPF) product, called PIAF-EPS (Prévision Immédiate Agrégée Fusionnée ensemble prediction system) and recently developed by Météo-France, to predict flash floods when used as input to an operational hydrological forecasting chain. For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reanalysed, using a hydrological-modelling chain similar to the one implemented in the French Vigicrues Flash operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the forecasts obtained with different deterministic QPFs from which PIAF-EPS is directly derived. The verification method applied in this work uses scores calculated on contingency tables and combines the forecasts issued on each 1 km2 pixel of the territory. This offers a detailed view of the forecast performances, covering the whole river network and including the small ungauged rivers. The results confirm the added value of the ensemble PIAF-EPS approach for flash flood forecasting, in comparison to the different deterministic scenarios considered.

Flood Monitoring System Based on IoT

Flooding is one of nature’s unavoidable calamities. It happens too quickly and affects far too many people and properties. Prior to this, the majority of previous systems exclusively focused on specific regions. Aside from that, the bulk of the people is unable to monitor and has no idea when the flood will occur since they lack access to weather information and data. By implementing an IoT (Internet of Things) based smart flood monitoring system, all of the shortcomings of the current system will be addressed. This project proposes a smart city flood monitoring and alerting system based on IoT. The open source Arduino electronic platform serves as the base for this project. This system uses water level and DHT11 sensors. Water level sensors were employed to measure the water level. This sensor will be in charge of sound navigation and range. It will function by sending out a short, high-frequency pulse, listening for the echo, and determining the level from there. A cheap digital temperature and humidity sensor is the DHT11. If the water level is unsafe, an alert message is sent and an alarm is activated to sound a flood alert. The water level and rainfall intensity in the area around the stream are monitored using NODEMCU. The LCD and LED functions are to display and signal the water level. The system will detect water level using a water level sensor and will automatically send a notification to the user’s smartphone via IoT technology via the Blynk application.

Design of Flood Barrier with Developed IoT-Based Flood Detection and Monitoring Systems

Due to Indonesia's tropical climate, it experiences frequent heavy rainfall, which is the primary cause of flooding. Despite various solutions implemented, the issue of flooding persists. Consumers demand an effective emergency flood protection solution to prevent water from entering homes at any time. This product is a flood barrier specifically designed for installation at the front door of houses. The development of this flood barrier product aims to address user needs and ensure flood resistance. To achieve this, the Kano Method and Quality Function Deployment (QFD) are utilized in the product design process, aligning it with customer desires. The Finite Element Analysis (FEA) is employed in testing to assess the flood barrier's ability to contain water effectively. Moreover, the data representing the user requirements is collected from residents in the Baleendah area. Those residents houses have been affected by flooding. This area faces significant economic losses due to flooding, estimated at IDR 5,490,292,000,000. Bandung Regency is projected to have the highest frequency of flooding incidents in West Java in 2022, contributing to the broader issue of flooding in the province and country. The developed flood barrier product has successfully met most customer needs, leading to customer satisfaction. The product offers a reasonable price, customer customization options, and user-friendly features. The integration of Internet of Things (IoT) capabilities has been well received by users. The flood barrier product also demonstrates its efficacy in retaining floodwater through its low equivalent elastic strain, equivalent stress, and deformation values against hydrostatic pressure. These attributes enable the product to withstand floodwaters and prevent their entry into households.

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

One of the most frequently occurring calamities around the world is the flood. For flood prone areas or countries, an essential part of their governance is flood management. The necessity to continuously review and analyse the adverse or ambient environmental conditions in real-time demands developing a monitoring system so that floods could be detected beforehand. This paper discusses different Internet of Things (IoT) based techniques and applications implemented for efficient flood monitoring and an early warning system and it is observed that in future, the combination of IoT and Synthetic Aperture Radar (SAR) data may be helpful to develop robust and secure flood monitoring and early warning system that provides effective and efficient mapping during natural disasters. The emerging technology in the discipline of computing is IoT, an embedded system that enables devices to gather real-time data to further store it in the computational devices using Wireless Sensor Networks (WSN) for further processing. The IoT based projects that can help collect data from sensors are an added advantage for researchers to explore in providing better services to people. These systems can be integrated with cloud computing and analyzing platforms. Researchers recently have focussed on mathematical modeling based flood prediction schemes rather than physical parametric based flood prediction. The new methodologies explore the algorithmic approaches. There have been many systems proposed based on analog technology to web-based and now using mobile applications. Further, alert systems have been designed using web-based applications that gather processed data by Arduino Uno Microcontroller which is received from ultrasonic and rain sensors. Additionally, the machine learning based embedded systems can measure different atmospheric conditions such as temperature, moisture, and rains to forecast floods by analyzing varying trends in climatic changes.

Iot Based Flood Detection, Alarm and Monitoring System Using Multilayer Perceptron and Regression

Flooding is a common natural event caused by heavy rainfall and high tides in the Philippines. While this disaster cannot be prevented, people can prepare themselves to face it. The City of Ilagan in the province of Isabela is one of the highly prone areas to flooding caused by the welling of the Cagayan River. Many communities are living in low-lying areas which most likely experiencing floods. An analysis of the location is conducted including the people living in the area. The analysis resulted in the development of an IoT-based technology for detection and early warning signals to people using sms notifications that can help lessen the difficulty of evacuation. The system uses Arduino UNO as a microcontroller where sensors are attached. These sensors are Light Detection and Ranging (LiDAR) for flood level measurement in feet (ft), Rain Gauge to measure the precipitation rate (mm/hr), and Flow Rate Meter to measure the fluctuation flow of the river in (L/hr). Data gathered from these sensors are processed and sent immediately to people living nearby to monitor the flood level in real-time. The predictive models are developed using the Pinacanauan River dataset taken from the river stations. The multilayer perceptron is used to develop the predictive model with 99% accuracy. The data from sensors are used also and processed using linear regression and calculated as 88% accurate and significant for prediction.

IOT-Based Flood Monitoring and Alarm System

Abstract: The IOT-based flood monitoring and alarm system is a cutting-edge technology solution that aims to provide real-time monitoring and early warning of potential flood disasters. Through the use of advanced sensors and wireless communication technologies, the system collects and analyses data on water levels and other relevant environmental factors to accurately predict and alert the authorities and residents of impending flood events. The system's ability to detect and report flood conditions promptly enables swift action to be taken, reducing the risks and damages associated with floods. This paper discusses the development and implementation of an IOT-based flood monitoring and alarm system, highlighting its advantages and potential impact on flood-prone areas

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

Wireless Sensor Network (WSN) of a flood monitoring system based on the Internet of Things (IoT)

Indonesia records exceptionally high rainfall, particularly during the rainy season when almost all areas of the country are consistently showered with heavy rain. Vigilance is therefore crucial due to the risk of flooding from overflowing rivers or dams. It is essential to develop flood monitoring systems to mitigate the risk and impact of flooding. This study aimed to design and build a flood monitoring system with parameters that support flood warnings. These include measurement of the water level using an ultrasonic sensor and rainfall using a tipping bucket-based hall sensor. The flood detection system was installed at Pondok Aren, Tangerang Selatan, Banten. A website was developed to display information on water levels and rainfall measurements every 10 minutes, as well as cumulative rainfall over 24 hours, presented in values, tables, and graphs. The device design included a warning feature in the form of a strobe light that would activate if the water level exceeded the minimum threshold in addition to providing rainfall status notifications. The system performed well in trials, with data transmitted to the database every 10 minutes. Raingauge sensors exhibited a 0.86% error rate, while the ultrasonic sensor showed an average error rate of just 0.25%.

Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network.

Lora Technology for Flood Monitoring and Warn System

Flooding is the most frequent natural disaster that has an impact on humans. There is a history of widespread damage from floods. The organizations and competent authorities use a variety of sophisticated techniques to track the flood level in areas with a high risk of flooding. The majority of these devices are quite expensive to use and maintain. In the proposed system, data from sensors including those measuring water level, water flow, and ultrasonic sensor can be sent to the cloud using a GSM device. If environmental condition threshold values rise, a warning message will be sent to the relevant authorities, who will then inform the residents of the flood-prone area. With the aid of such a system, both commercial and public organizations can develop their flood mitigation strategies before the situation worsens. The most widely used mobile messaging platform, the Global System for Mobile Communication (GSM), is used to send the SMS to the villages as a warning simultaneously a buzzer will ring which is placed in every village of the flood-prone area. This buzzer will work with the technology of LoRa transceiver, which had a long range of communication.

Real Time Flood Detection, Alarm and Monitoring System Using Image Processing and Multiple Linear Regression

Real Time Flood Detection, Alarm and Monitoring System Using Image Processing and Multiple Linear Regression

Deep Learning-Based Flood Area Extraction for Fully Automated and Persistent Flood Monitoring Using Cloud Computing

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning.

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting.

Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations, radar quantitative precipitation estimates (QPE), and radar fusion data during Typhoon Fitow (2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China, as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows: (1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%. (2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details. (3) One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result—all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Development of Web-Based Flood Detection System and Flood Control Using Nodemcu

Internet of Things is a concept and method for remote control, monitoring, data transmission, and various other tasks. For some areas in Indonesia, flooding may be unavoidable, but this can be done to minimize the number of disaster losses by providing early warnings if there are indications that a flood will occur. This study will create an internet of things system to monitor the river’s water level and the sluice gate control system. The manufacture of this system will be made in the form of a prototype, to detect the water level will use ultrasonic sensors, and to drive the floodgates in this system will use a stepper motor. In this system, to determine the speed of movement of the openings of the sluice gate is determined based on the water level obtained as input data and calculated using fuzzy logic algorithms. The fuzzy logic algorithm here is one of the methods used in the field of control system studies. Testing on the fuzzy algorithm the author uses the fuzzy logic toolbox in the MATLAB application as a comparison to the fuzzy algorithm made on the prototype, from the results of testing the flood monitoring system runs perfectly by providing the appropriate output results and the average error of controlling the floodgates is 0.58%.

Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.

The role of satellite information in forecasting, modeling, and mapping the 2019 Mozambique flood

AbstractThe application and value of the Global Flood Monitoring System (GFMS) and various remote‐sensing‐based flood products are examined in the context of the severe flood event in Mozambique associated with Cyclone Idai in March 2019. Short‐term forecasts of rainfall and flooding are shown to be useful and validated to some degree by satellite‐based rainfall from the Global Precipitation Monitoring (GPM) Integrated Multi‐satellitE Retrievals for GPM (IMERG) product. However, detailed validation of the satellite‐based rainfall for this event is limited and examination of other similar cases indicates a possible underestimate at high rainfall amounts. GFMS inundation based on the IMERG rainfall and models are compared to Synthetic Aperture Radar (SAR) and other inundation estimates. Results indicate a generally good correspondence, but with the GFMS calculations underestimating the area of broad flooding, but overestimating in smaller streams. The GFMS underestimation compared to a SAR‐based estimate in one particular area seems related to rainfall underestimation. Similar generally good results were found when comparing the GFMS calculations to the FloodScan passive microwave‐based inundation. The various inundation estimates are available at different latencies and likely have different accuracies, indicating a need for integration of these types of information to provide the user community with the best consolidated information and in a timely manner.